Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [1]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using Theano backend.
Using cuDNN version 5110 on context None
Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:23:00.0)
There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [2]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [3]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[11])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [4]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

In [5]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
num_faces_detected_humans = 0
false_neg_humans = []
for human in human_files_short:
    if face_detector(human):
        num_faces_detected_humans += 1
    else:
        false_neg_humans.append(human)



num_faces_detected_dogs = 0
false_pos_dogs = []
for dog in dog_files_short:
    if face_detector(dog):
        num_faces_detected_dogs += 1
        false_pos_dogs.append(dog)

        
print('\n=========================== Result =====================================')
        
print(str(num_faces_detected_humans) + "% images in the human_files_short set was identified as having one or more human faces. \nThe expected results was 100%.")
print('\n')
print(str(num_faces_detected_dogs) + "% images in the dog_files_short set was identified as having one or more human faces. \nThe expected results was 0%.")

# NOTE: Since we are dealing with 100 images, the count is the percentage as well
=========================== Result =====================================
99% images in the human_files_short set was identified as having one or more human faces. 
The expected results was 100%.


11% images in the dog_files_short set was identified as having one or more human faces. 
The expected results was 0%.
In [6]:
print("Humans which could not be detected")
print("==============================================================================")
for human in false_neg_humans:
    img = cv2.imread(human)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # display the image, along with bounding box
    plt.imshow(cv_rgb)
    plt.show()
    
print("Dogs which were detected to have human face")
print("==============================================================================")
fig = plt.figure(figsize=(20, 20))
cols = 3
rows = int(len(false_pos_dogs) / cols) + 1

for i, d in enumerate(false_pos_dogs):
    img = cv2.imread(d)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # display the image, along with bounding box
    ax = fig.add_subplot(rows, cols, i+1)
    ax.imshow(cv_rgb)
plt.show()
    
Humans which could not be detected
==============================================================================
Dogs which were detected to have human face
==============================================================================

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer:

On inspecting the images that resulted in false positive or false negative, it is obvious (to me) that the classifier should have detected them correctly. Reason being that there are distinctive features which differentiate humans and dogs in those photos. (There is one dog photo which has a human face too, making it an exception.)

There is an inverse relationship between how much instructions a user has to follow get get a technology to work, and it's adoption and profitability. It is okay in my opinion to set some expectations with the user. However in the above cases, our algorithm could have done better.

Reading about the Viola&Jones algorithm[1] for face detection (which uses Haar feature selection), came to know that "in order to be detected, the entire face must point towards the camera and should not be tilted to either side." That explains why George Bush's face was not correctly classified.

Taking inspiration from ImageNet competitons, models based on CNN are the most promising approaches as of now for anything to do with images. In addition to learning higher level concepts (cars, humans, flower) from low level representations (edges and shapes), we can also make use of image augmentation to train the model to learn to be invariant to rotation, translation and scale.

[1] https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework


Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [7]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [8]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [9]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [10]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

In [11]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

num_faces_detected_humans = 0
false_pos_humans = []
for human in human_files_short:
    if dog_detector(human):
        false_pos_humans.append(human)
        num_faces_detected_humans += 1


num_faces_detected_dogs = 0
false_neg_dogs = []
for dog in dog_files_short:
    if dog_detector(dog):
        num_faces_detected_dogs += 1
    else:
        false_neg_dogs.append(dog)

        
print('\n=========================== Result =====================================')
        
print(str(num_faces_detected_humans) + "% images in the human_files_short set was identified as having one or more detected dogs. \nThe expected results was 0%.")
print('\n')
print(str(num_faces_detected_dogs) + "% images in the dog_files_short set was identified as having one or more detected dogs. \nThe expected results was 100%.")

# NOTE: Since we are dealing with 100 images, the count is the percentage as well
=========================== Result =====================================
0% images in the human_files_short set was identified as having one or more detected dogs. 
The expected results was 0%.


100% images in the dog_files_short set was identified as having one or more detected dogs. 
The expected results was 100%.

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [12]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████| 6680/6680 [00:40<00:00, 165.52it/s]
100%|██████████| 835/835 [00:04<00:00, 184.98it/s]
100%|██████████| 836/836 [00:04<00:00, 188.04it/s]
In [13]:
train_tensors.shape
Out[13]:
(6680, 224, 224, 3)

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer:

Frankly, I took the model that was taught in the lecture by Alexis, changed the dimensions and executed it. I got a test accuracy of 12.6%.

Thinking back, the following best practices come to mind to get good predictions:

  • We want the depth of the comvolution layers to increase progressively, so that the representations we learn move from corners and edges to ears and eyes
  • We want the size of each convolution layer to decrease progressively, so that execution speeds up (less weights to train)
  • We keep the padding as 'same' to not loose any details at the edges
  • Dropouts are good to make sure all the nodes get enough training

The model has all of these properties.

In [14]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model = Sequential()

model = Sequential()
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(133, activation='softmax'))

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 32)      2080      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 64)        8256      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 50176)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 500)               25088500  
_________________________________________________________________
dropout_2 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               66633     
=================================================================
Total params: 25,165,677
Trainable params: 25,165,677
Non-trainable params: 0
_________________________________________________________________

Compile the Model

In [15]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [16]:
from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/5
6660/6680 [============================>.] - ETA: 0s - loss: 4.7908 - acc: 0.0206Epoch 00000: val_loss improved from inf to 4.45277, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 129s - loss: 4.7901 - acc: 0.0207 - val_loss: 4.4528 - val_acc: 0.0551
Epoch 2/5
6660/6680 [============================>.] - ETA: 0s - loss: 4.3205 - acc: 0.0559Epoch 00001: val_loss improved from 4.45277 to 4.36823, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 129s - loss: 4.3198 - acc: 0.0560 - val_loss: 4.3682 - val_acc: 0.0551
Epoch 3/5
6660/6680 [============================>.] - ETA: 0s - loss: 3.9192 - acc: 0.1135Epoch 00002: val_loss improved from 4.36823 to 4.09150, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 130s - loss: 3.9187 - acc: 0.1136 - val_loss: 4.0915 - val_acc: 0.1030
Epoch 4/5
6660/6680 [============================>.] - ETA: 0s - loss: 3.2860 - acc: 0.2251Epoch 00003: val_loss improved from 4.09150 to 4.03769, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 130s - loss: 3.2873 - acc: 0.2249 - val_loss: 4.0377 - val_acc: 0.1150
Epoch 5/5
6660/6680 [============================>.] - ETA: 0s - loss: 2.4098 - acc: 0.4135Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 130s - loss: 2.4107 - acc: 0.4133 - val_loss: 4.2263 - val_acc: 0.1162
Out[16]:
<keras.callbacks.History at 0x7f43ea2fd6d8>

We got our best model within the first 5 epocs itself.

Load the Model with the Best Validation Loss

In [17]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [18]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 12.6794%

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

In [19]:
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [20]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________

Compile the Model

In [21]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [22]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=0)
Epoch 00000: val_loss improved from inf to 10.66051, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00001: val_loss improved from 10.66051 to 10.12370, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00002: val_loss improved from 10.12370 to 9.80544, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00003: val_loss improved from 9.80544 to 9.72869, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00004: val_loss improved from 9.72869 to 9.65099, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00005: val_loss improved from 9.65099 to 9.52070, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00006: val_loss improved from 9.52070 to 9.49770, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00007: val_loss improved from 9.49770 to 9.49285, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00008: val_loss improved from 9.49285 to 9.49148, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00009: val_loss improved from 9.49148 to 9.34827, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00010: val_loss did not improve
Epoch 00011: val_loss improved from 9.34827 to 9.13435, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00012: val_loss improved from 9.13435 to 9.11256, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00013: val_loss improved from 9.11256 to 9.07326, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00014: val_loss improved from 9.07326 to 9.03869, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00015: val_loss improved from 9.03869 to 8.90261, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00016: val_loss improved from 8.90261 to 8.78609, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00017: val_loss improved from 8.78609 to 8.51496, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00018: val_loss improved from 8.51496 to 8.45803, saving model to saved_models/weights.best.VGG16.hdf5
Epoch 00019: val_loss improved from 8.45803 to 8.23120, saving model to saved_models/weights.best.VGG16.hdf5
Out[22]:
<keras.callbacks.History at 0x7f43c130b0b8>

Load the Model with the Best Validation Loss

In [23]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [24]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 40.5502%

Predict Dog Breed with the Model

In [25]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [26]:
### I have decided to go with Resnet50

bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_resnet50 = bottleneck_features['train']
valid_resnet50 = bottleneck_features['valid']
test_resnet50 = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

My target was to get the best accuracy with least number of total parameters.

Towards that, I had to select the number of layers and the size of each layer. ('ReLU' as activation function and 'Rmsprop' as the optimizer is a given for CNN's)

I tried with 3 options, and the best one turned out to be the simplest model.

  • Option 1: Only one Global Average Pooling layer
  • Option 2: Global Average Pooling Layer, Dense layer of 128 followed by a dropout
  • Option 2: Global Average Pooling Layer, Dense layer of 128 followed by a dropout, followed by Dense layer of 256 followed by a dropout.

Option 1 gave 82%, Option 2 gave 82% and Option 3 gave 77.3%.

I also tried by increasing the number of nodes per layer, but nothing could beat our simple architecture.

This is a good lesson, that complex is not always better.

In [27]:
### My final architecture
Resnet50_model = Sequential()
Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_resnet50.shape[1:]))
#Resnet50_model.add(Dense(128, activation='relu'))
#Resnet50_model.add(Dropout(0.2))
#Resnet50_model.add(Dense(256, activation='relu'))
#Resnet50_model.add(Dropout(0.2))
Resnet50_model.add(Dense(133, activation='softmax'))
Resnet50_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               272517    
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model

In [28]:
### TODO: Compile the model.
Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [29]:
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5', 
                               verbose=1, save_best_only=True, monitor='val_acc')

Resnet50_model.fit(train_resnet50, train_targets, 
          validation_data=(valid_resnet50, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=0)
Epoch 00000: val_acc improved from -inf to 0.75928, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00001: val_acc improved from 0.75928 to 0.80000, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00002: val_acc improved from 0.80000 to 0.80719, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00003: val_acc improved from 0.80719 to 0.81198, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00004: val_acc did not improve
Epoch 00005: val_acc improved from 0.81198 to 0.82036, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00006: val_acc did not improve
Epoch 00007: val_acc improved from 0.82036 to 0.82994, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00008: val_acc did not improve
Epoch 00009: val_acc improved from 0.82994 to 0.83593, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00010: val_acc did not improve
Epoch 00011: val_acc did not improve
Epoch 00012: val_acc improved from 0.83593 to 0.83713, saving model to saved_models/weights.best.Resnet50.hdf5
Epoch 00013: val_acc did not improve
Epoch 00014: val_acc did not improve
Epoch 00015: val_acc did not improve
Epoch 00016: val_acc did not improve
Epoch 00017: val_acc did not improve
Epoch 00018: val_acc did not improve
Epoch 00019: val_acc did not improve
Out[29]:
<keras.callbacks.History at 0x7f43c0ef9748>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

In [30]:
### TODO: Load the model weights with the best validation loss.
Resnet50_model.load_weights('saved_models/weights.best.Resnet50.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [31]:
### TODO: Calculate classification accuracy on the test dataset.
Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_resnet50]

# report test accuracy
test_accuracy = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 82.5359%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [32]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

from extract_bottleneck_features import *

def Resnet50_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = Resnet50_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]
In [33]:
# Testing

index = 0
breed = Resnet50_predict_breed(test_files[index])
# load color (BGR) image
img = cv2.imread(test_files[index])
# convert BGR image to grayscale
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(img_rgb)
plt.show()
print('The breed is {0}. File parsed was {1}'.format(breed, test_files[index]))
The breed is Dalmatian. File parsed was dogImages/test/057.Dalmatian/Dalmatian_04056.jpg

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [34]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
In [35]:
def predict(image_path):
    # Step 1: Detect if this is a dog (if not, consider it human face because our datasets have only dogs and humans.)
    isDog = dog_detector(image_path)
    # Let's also record what our human face detector says
    isHuman = face_detector(image_path)
    breed = Resnet50_predict_breed(image_path)
    return isDog, isHuman, breed

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer:

In [36]:
def detect_human_dog(file):
    isDog, isHuman, breed = predict(file)
    img = cv2.imread(file)
    img1_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    breed_folder = [item for item in sorted(glob("dogImages/train/*/")) if item[20:-1] == breed]
    search = "{0}*".format(breed_folder[0])
    breed_photos = [item for item in sorted(glob(search))]
    
    img = cv2.imread(breed_photos[0])
    img2_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    fig = plt.figure(figsize=(10, 10))
    ax1 = fig.add_subplot(1, 2, 1)
    ax2 = fig.add_subplot(1, 2, 2)
    ax1.imshow(img1_rgb)
    ax2.imshow(img2_rgb)
    plt.show()
    
    
    if isDog:
        print('This is a Dog. Breed is {0}. On the right is another photo of the same breed.'.format(breed))
    else:
        print('This is a Human. Looks like the breed {0}.'.format(breed))
    print("==============================================================================")
In [37]:
detect_human_dog(human_files[190])
detect_human_dog(human_files[101])
detect_human_dog(human_files[122])

detect_human_dog(test_files[10])
detect_human_dog(test_files[101])
detect_human_dog(test_files[122])
This is a Human. Looks like the breed American_water_spaniel.
==============================================================================
This is a Human. Looks like the breed English_springer_spaniel.
==============================================================================
This is a Human. Looks like the breed Silky_terrier.
==============================================================================
This is a Dog. Breed is Afghan_hound. On the right is another photo of the same breed.
==============================================================================
This is a Dog. Breed is Australian_cattle_dog. On the right is another photo of the same breed.
==============================================================================
This is a Dog. Breed is Xoloitzcuintli. On the right is another photo of the same breed.
==============================================================================
  • The solution could actually correctly classify human face and dogs.
  • The dog breed was correctly detected for dogs. The resembling dog breed for humans convincing to some extent. (Except maybe for Meryl Streep)

The approach can be improved by:

  • Training with more data to make it more robust
  • By augmenting data to make the model invariant to rotation, scaling, translation etc.
  • Working further on the architecture